Data & Analytics Solutions Architect || Toronto, ON - Onsite

Role: Data & Analytics Solutions Architect

Location: Toronto, ON

Work Model: Onsite

Job Type: Full Time

Job Description

We are seeking an experienced Data & Analytics Solutions Architect with strong expertise in Enterprise Data Architecture, Hadoop ecosystems, Big Data Engineering, and Data Platform Modernization. The ideal candidate will have 14+ years of experience designing and implementing scalable, secure, and high-performance data platforms that support enterprise analytics, reporting, and AI workloads.

The role requires hands-on knowledge of Hadoop, HDFS, YARN, Spark, PySpark, Hive, Kafka, and distributed data processing, combined with the ability to define end-to-end architecture, lead modernization initiatives, establish governance standards, and provide technical leadership across large-scale enterprise data programs. Experience in the BFSI domain and knowledge of regulatory, security, and compliance requirements are highly desirable.

Key Responsibilities

•Engage with business and technology stakeholders to understand business objectives, data platform requirements, and modernization goals.

•Define end-to-end architecture for Hadoop and Big Data platforms supporting analytics, reporting, and AI workloads.

•Design scalable, secure, and high-performance Hadoop ecosystems using HDFS, YARN, Spark, Kafka, Hive, and related technologies.

•Architect enterprise-scale data ingestion, batch processing, streaming, and Data Lake solutions.

•Design and optimize distributed data processing solutions using Spark, Hive, HBase, and MapReduce.

•Define data models, Data Lake zones, governance frameworks, metadata management, data lineage, and security standards.

•Lead Hadoop platform modernization, migration, and optimization initiatives to improve performance, scalability, and operational efficiency.

•Establish architecture standards, reusable frameworks, engineering best practices, and DataOps operating models.

•Create High-Level Designs (HLDs) and Low-Level Designs (LLDs) and guide implementation teams throughout the delivery lifecycle.

•Provide technical leadership across solution design, development, testing, migration, deployment, and production support.

•Conduct architecture reviews, identify technical risks and dependencies, and recommend mitigation strategies.

•Drive performance tuning, capacity planning, platform reliability, workload optimization, and infrastructure cost efficiency.

•Ensure appropriate security and governance controls, including access management, encryption, privacy, auditability, and regulatory compliance.

•Collaborate with data engineers, platform teams, architects, vendors, and client stakeholders to deliver enterprise data solutions.

•Support solutioning, RFP responses, effort estimation, technical presentations, and client workshops.

•Mentor architects and engineers and contribute to hiring, capability development, and knowledge sharing.

•Develop reusable accelerators, reference architectures, implementation frameworks, and technical best practices for Big Data platforms.

Required Skills & Experience

•14+ years of experience in Enterprise Data Architecture, Hadoop Ecosystem, Big Data Engineering, and Data Platform Modernization.

•Strong hands-on expertise in Hadoop, HDFS, YARN, Hive, Apache Spark, PySpark, Kafka, SQL, Python, Java, and Scala.

•Experience designing and implementing large-scale Big Data and Data Lake architectures for analytics, reporting, and AI workloads.

•Strong understanding of Hadoop platform architecture, including HDFS, YARN, high availability, cluster management, workload optimization, and scalability.

•Expertise in batch and distributed data processing using Spark, Hive, HBase, and MapReduce.

•Experience building data ingestion and streaming solutions using Kafka and Hadoop ecosystem tools.

•Strong knowledge of ETL/ELT, data integration, data quality, metadata management, data lineage, observability, and DataOps.

•Experience with Data Lakes, Data Warehousing, Enterprise Data Models, Master Data Management, and Data Governance frameworks.

•Understanding of security and governance controls, including Kerberos, Apache Ranger, encryption, privacy, auditability, compliance, and controlled data access.

•Proven experience in architecture definition, HLD/LLD creation, migration planning, technical governance, production readiness, and performance optimization.

•Experience supporting platform modernization, cloud adoption, solutioning, effort estimation, and client-facing technical discussions.

•Good understanding of BFSI regulatory, security, privacy, resilience, and audit requirements.

Preferred Qualifications

•Certifications in Hadoop, Spark, Cloud Data Engineering, Enterprise Architecture, Data Management, Security, or Big Data Platforms.

•Experience with enterprise-scale Hadoop modernization and cloud migration programs.

•Strong stakeholder management, technical communication, and problem-solving skills.

•Ability to mentor technical teams and establish reusable architecture and engineering practices.